CCSS: Quickest Detection Under Energy Constraints

无线传感器网络通常用于监测周围环境的异常变化。这些变化通常意味着某些活动的严重后果，如结构故障或化学/气体泄漏等。最快检测是一个框架，重点是设计顺序检测算法，以尽可能快速可靠地识别这些变化，以便我们可以赢得宝贵的时间采取适当的行动。在大多数现有的作品中，它是假设，有多少和传感器可以采取样本没有约束。这种假设可能不适用于传感器网络中的许多应用，其传感器由具有有限能量的电池或从环境中收集的可再生能源供电。重要的是设计新颖的具有能量约束的最快检测算法，使得所设计的算法可以用于使用由电池或可再生能源供电的传感器以最小延迟检测异常活动。能源约束提出了重大的挑战和独特的功能，以最快的检测问题。设计自适应传感策略至关重要，该策略依赖于从迄今为止采集的样本中提取的信息和电池的能量水平来做出样本和检测决策。为此，使用最优停止理论的工具，该项目旨在实现以下目标：1）表征具有额外能量约束的问题的最优检测方案; 2）了解与这些能量约束相关的性能损失;以及3）设计低复杂度但渐近最优的检测方案。为了实现这些目标，该项目将侧重于两个研究重点。在第一个研究重点中，该项目将重点关注对传感器允许进行的观测总数有严格限制的场景。本文所设计的算法对能量有限的电池供电的传感器具有一定的参考价值。在第二个研究重点中，该项目将侧重于具有随机能量约束的场景。所设计的算法适用于由可再生能源供电的传感器。

Wireless sensor networks are commonly deployed to monitor abnormal changes in their surrounding environment. These changes typically imply certain activities of severe consequences, such as structure failure or chemical/gas leak, etc. Quickest detection is a framework that focuses on the design of sequential detection algorithms to identify such changes as quickly and reliably as possible so that we can win valuable time to take proper actions. In most of existing works, it is assumed that there is no constraint on how many and when sensors can take samples. This assumption may not hold for many applications in sensor networks whose sensors are powered either by battery with limited energy or renewable energy harvested from the environment. It is important to design novel quickest detection algorithms with energy constraints so that the designed algorithms can be used to detect abnormal activities using sensors powered by battery or renewable energy with a minimal delay. The energy constraints present significant challenges and unique features to quickest detection problems. It is crucial to design adaptive sensing strategies that rely on information extracted from samples taken so far and energy level at the battery to make sample and detection decisions. Towards this end, using tools from optimal stopping theory, the project aims to achieve the following goals: 1) to characterize the optimal detection schemes for problems with additional energy constraints; 2) to understand the performance loss associated with these energy constraints; and 3) to design low-complexity but asymptotically optimal detection schemes. To achieve these goals, the project will focus on two research thrusts. In the first research thrust, the project will focus on scenarios with a hard constraint on the total number of observations that the sensor is allowed to take. The designed algorithms in this thrust will be useful for sensors powered by battery with limited energy. In the second research thrust, the project will focus on scenarios with a stochastic energy constraint. The designed algorithms are suitable for sensors powered by renewable energy.